Penilaian Kredit Menggunakan Algoritma XGBoost dan Logistic Regression
Abstract
Penilaian kredit merupakan suatu proses atau sistem
yang digunakan oleh lembaga pembiayaan atau bank untuk
menilai kelayakan seseorang yang mengajukan pinjaman. Hal
ini sangat diperlukan untuk menghindari kerugian akibat gagal
bayar. Menanggapi hal tersebut dibutuhkan sebuah metode
yang efisien, cepat dan akurat untuk mengklasifikasikan layak
atau tidaknya seseorang untuk diberikan pinjaman. Penulis
mengusulkan metode machine learning dan membandingkan
algoritma XGBoost dan logistic regression. Setelah dilatih dan
diuji dengan stratified kfold cross validation, XGBoost
menghasilkan rata-rata akurasi 85,51%; F1 Score 83,81%;
precision 83,80% dan recall 84,04% sedangkan logisitc
regression menghasilkan rata-rata akurasi 85,94%; F1 Score
85,36%; precision 80,08%; dan recall 91,52%. Kedua algoritma
dapat mengklasifikasikan layak atau tidaknya seseorang untuk
diberikan pinjaman dengan baik, sehingga dapat digunakan
untuk membantu institusi keuangan maupun para analis kredit.
Keywords
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DOI: https://doi.org/10.30591/jpit.v8i1.4337
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